# Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks

arXiv: https://arxiv.org/abs/1909.01311 · HTML: https://mlsanity.com/paper/1909.01311/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks](https://mlsanity.com/paper/2210.02157/) (2022)
- [Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits](https://mlsanity.com/paper/2303.18187/) (2023)
- [Learning in Deep Neural Networks Using a Biologically Inspired Optimizer](https://mlsanity.com/paper/2104.11604/) (2021)
- [Orthogonal Weight Modification Enhances Learning Scalability and Convergence Efficiency without Gradient Backpropagation](https://mlsanity.com/paper/2602.22259/) (2026)
- [Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data](https://mlsanity.com/paper/2605.18557/) (2026)
- [Biologically Plausible Training of Deep Neural Networks Using a Top-down Credit Assignment Network](https://mlsanity.com/paper/2208.01416/) (2022)

## Abstract (arXiv)

While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and backward passes are completed. Not only do these constraints preclude biological plausibility, but they also hinder the development of low-cost adaptive smart sensors at the edge, as they severely constrain memory accesses and entail buffering overhead. In this work, we show that the one-hot-encoded labels provided in supervised classification problems, denoted as targets, can be viewed as a proxy for the error sign. Therefore, their fixed random projections enable a layerwise feedforward training of the hidden layers, thus solving the weight transport and update locking problems while relaxing the computational and memory requirements. Based on these observations, we propose the direct random target projection (DRTP) algorithm and demonstrate that it provides a tradeoff between accuracy and computational cost that is suitable for adaptive edge computing devices.